Approximation Algorithms for Network Design Problems

Abstract

We consider different variants of network design problems. Given a set of points in the plane we search for a shortest interconnection of them. In this general formulation the problem is known as Steiner tree problem. We consider the special case of octilinear Steiner trees in the presence of hard and soft obstacles. In an octilinear Steiner tree the line segments connecting the points are allowed to run either in horizontal, vertical or diagonal direction. An obstacle is a connected region in the plane bounded by a simple polygon. No line segment of an octilinear Steiner tree is allowed to lie in the interior of a hard obstacle. If we intersect a Steiner tree with the interior of a soft obstacle, no connected component of the induced subtree is allowed to be longer than a given fixed length. We provide polynomial time approximation schemes for the octilinear Steiner tree problem in the presence of hard and soft obstacles. These were the first presented approximation schemes introduced for the problems. Additionally, we introduce a (2+epsilon)-approximation algorithm for soft obstacles. We then turn to Euclidean group Steiner trees. Instead of a set of fixed points we get for each point a set of potential locations (combined into groups) and we need to pick only one location of each group. The groups we consider lie inside disjoint regions fulfilling a special property so-called alpha-fatness. Roughly speaking, the term alpha-fat specifies the shape of the region in comparison to a disk. We give the first approximation algorithm for this problem and achieve an approximation ratio of (1+epsilon)(9.093alpha +1). Last, we consider Manhattan networks. They are allowed to contain edges only in horizontal and vertical direction. In contrast to Steiner trees they contain a shortest path between each pair of points. We introduce insights into the structure of Manhattan networks, particularly in the context of so-called staircases. We give three new approximation algorithms for the Manhattan network problem, the first with approximation ratio 3 and two algorithms with ratio 2. To this end we introduce two algorithms for the Manhattan network problem of staircases. The first algorithm solves the problem to optimality the second yields a 2-approximation. Variants of both algorithms are already known in the literature. Since we use a slightly different definition of staircases and we need special properties of them, we adopt the algorithms to our situation. The 2-approximation algorithms achieve the best known approximation ratio of an algorithm for the Manhattan network problem known so far. Last we give an idea how we could possibly find an algorithm with better approximation ratio.